Simple and flexible Bayesian inferences for standardized regression coefficients

ABSTRACT

In statistical practice, inferences on standardized regression coefficients are often required, but complicated by the fact that they are nonlinear functions of the parameters, and thus standard textbook results are simply wrong. Within the frequentist domain, asymptotic delta methods can be used to construct confidence intervals of the standardized coefficients with proper coverage probabilities. Alternatively, Bayesian methods solve similar and other inferential problems by simulating data from the posterior distribution of the coefficients. In this paper, we present Bayesian procedures that provide comprehensive solutions for inferences on the standardized coefficients. Simple computing algorithms are developed to generate posterior samples with no autocorrelation and based on both noninformative improper and informative proper prior distributions. Simulation studies show that Bayesian credible intervals constructed by our approaches have comparable and even better statistical properties than their frequentist counterparts, particularly in the presence of collinearity. In addition, our approaches solve some meaningful inferential problems that are difficult if not impossible from the frequentist standpoint, including identifying joint rankings of multiple standardized coefficients and making optimal decisions concerning their sizes and comparisons. We illustrate applications of our approaches through examples and make sample R functions available for implementing our proposed methods.     

多元抽样调查两级抽样中比率型估计量的平均精度

本文通过多元抽样调查的两种抽样方法,得到了总体均值的无偏估计量,并且证明了和一元情形一样好的结果,即在一定条件下比率型估计量的平均精度不劣于无偏估计量的精度。     

Hormone Treatment in Transsexuals

No abstract available for this article.     

Social network analysis with respondent-driven sampling data: A study of racial integration on campus

This paper presents respondent-driven sampling (RDS) as a viable method of sampling and analyzing social networks with survey data. RDS is a network based sampling and analysis method that provides a middle ground compliment to ego-centric and saturated methods of social network analysis. The method provides survey data, similar to ego-centric approaches, on individuals who are connected by behaviorally documented ties, allowing for macro-level analysis of network structure, similar to that supported by saturated approaches. Using racial interaction of university undergraduates as an empirical example, the paper examines whether and to what extent racial diversity at the institutional level is reflected as racial integration at the interpersonal level by testing hypotheses regarding the quantity and quality of cross-race friendships. The primary goal of this article, however, is to introduce RDS to the network community and to stimulate further research toward the goal of expanding the analytical capacity of RDS. Advantages, limitations, and areas for future research to network analysis using RDS are discussed.     

Omission and commission errors in network cognition and network estimation using ROC curve

Network studies on cognitive social structures collect relational data on respondents’ direct ties and their perception of ties among all other individuals in the network. When reporting their perception networks, respondents commit two types of errors, namely, omission (false negatives) and commission (false positives) errors. We first assess the relationship between these two error types, and their contributions on overall respondent accuracy. Next we propose a method for estimating networks based on perceptions of a random sample of respondents from a bounded social network, which utilizes the receiver operator characteristic curve for balancing the tradeoffs between omission and commission errors.     

The Inverse Relation Between Risks and Benefits: The Role of Affect and Expertise

Although risk and benefits of risky activities are positively correlated in the real world, empirical results indicate that people perceive them as negatively correlated. The common explanation is that confounding benefits and losses stems from affect. In this article, we address the issue that has not been clearly established in studies on the affect heuristic: to what extent boundary conditions, such as judgments’ generality and expertise, influence the presence of the inverse relation in judgments of hazards. These conditions were examined in four studies in which respondents evaluated general or specific benefits and risks of “affect‐rich” and “affect‐poor” hazards (ranging from investments to applications of stem cell research). In line with previous research, affect is defined as good or bad feelings integral to a stimulus. In contrast to previous research, affect is considered as related both to personal feelings and to social controversies associated with a hazard. Expertise is related to personal knowledge (laypersons vs. experts) as well as to objective knowledge (targets well vs. poorly known to science). The direct comparison of the input from personal and objective ignorance into the inverse relation has not been investigated previously. It was found that affect invoked by a hazard guides general but not specific judgments of its benefits and risks. Technical expertise helps to avoid simplified evaluations of consequences as long as they are well known to science. For new, poorly understood hazards (e.g., stem cell research), expertise does not protect from the perception of the inverse relation between benefits and risks.     

Sampling Strategies for Evaluating the Rate of Adventitious Transgene Presence in Non‐Genetically Modified Crop Fields

According to E.U. regulations, the maximum allowable rate of adventitious transgene presence in non‐genetically modified (GM) crops is 0.9%. We compared four sampling methods for the detection of transgenic material in agricultural non‐GM maize fields: random sampling, stratified sampling, random sampling + ratio reweighting, random sampling + regression reweighting. Random sampling involves simply sampling maize grains from different locations selected at random from the field concerned. The stratified and reweighting sampling methods make use of an auxiliary variable corresponding to the output of a gene‐flow model (a zero‐inflated Poisson model) simulating cross‐pollination as a function of wind speed, wind direction, and distance to the closest GM maize field. With the stratified sampling method, an auxiliary variable is used to define several strata with contrasting transgene presence rates, and grains are then sampled at random from each stratum. With the two methods involving reweighting, grains are first sampled at random from various locations within the field, and the observations are then reweighted according to the auxiliary variable. Data collected from three maize fields were used to compare the four sampling methods, and the results were used to determine the extent to which transgene presence rate estimation was improved by the use of stratified and reweighting sampling methods. We found that transgene rate estimates were more accurate and that substantially smaller samples could be used with sampling strategies based on an auxiliary variable derived from a gene‐flow model.     

A cost model for deferred state life test plans with replacement

This paper presents a cost model for the deferred state life test plan, which is one of the lot-by-lot acceptance sampling plans by attributes. In most sampling plans, the decision to accept or reject a submitted lot depends only on the sampling test results of the lot concerned; other information will not be considered. Deferred state life test plan is a sampling plan which uses information about subsequent lots for making decisions to accept or reject the current lot. The objective of this paper is to evaluate the total test cost of using the deferred state life test plan to see whether the use of this life test plan can reduce the total cost of the sampling test. In order to calculate the expected total test cost of the deferred state life test plan, a cost model is developed for the deferred state life test plan with replacement. Also, a cost comparison is made between a deferred state life test plan and a comparable military standard sampling plan which offers the same level of producer's and consumer's protection. The results show that the deferred state life test plan consistently reduces the overall cost of sampling tests over a variety of test conditions.     

A procedure for selection of a gamma-Poisson single sampling plan by attributes

Design and evaluation of sampling plans by attributes and by variables are important aspects in the area of acceptance sampling research. Various procedures for the selection of conventional single sampling by attributes have been developed and are available in the literature. This paper presents a design methodology and tables for the selection of parameters of single sampling plans for specified requirements (strengths) under the conditions of a gamma prior and Poisson sampling distribution. The relative efficiency of gamma-Poisson single sampling plans over conventional plans is discussed through empirical illustrations.